With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that were written by humans, particularly uncovering the underlying discourse structures of texts beyond their surface structures. Introducing a novel methodology, we leverage hierarchical parse trees and recursive hypergraphs to unveil distinctive discourse patterns in texts produced by both LLMs and humans. Empirical findings demonstrate that, although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. Notably, incorporating hierarchical discourse features enhances binary classifiers' overall performance in distinguishing between human-written and machine-generated texts, even on out-of-distribution and paraphrased samples. This underscores the significance of incorporating hierarchical discourse features in the analysis of text patterns. The code and dataset will be available at [TBA].

翻译：随着大型语言模型（LLM）的出现，人工撰写文本与机器生成文本之间的界限日益模糊。本文深入探究如何识别人类所写文本中可辨别且独特的语言属性，尤其关注揭示文本表层结构之外潜在的话语结构。我们提出一种新颖方法，利用层次化解析树和递归超图，揭示LLM与人类生成文本中独特的话语模式。实验结果表明，尽管LLM和人类均会生成受特定领域影响的不同话语模式，但人类撰写的文本展现出更多结构变异性，反映了人类写作在不同领域的微妙特质。值得注意的是，融入层次化话语特征能提升二元分类器在区分人工撰写与机器生成文本时的整体性能，即便面对分布外样本和释义样本也是如此。这凸显了在文本模式分析中融入层次化话语特征的重要性。代码与数据集将在[待定]处公开。